OPTIMAL PLACEMENT OF DISTRIBUTED GENERATION

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OPTIMAL PLACEMENT OF DISTRIBUTED GENERATION

  1. 1. “OPTIMAL PLACEMENT AND SIZING OF MULTI- DISTRIBUTED GENERATION (DG) INCLUDING DIFFERENT LOAD MODELS USING PSO” A Dissertation Submitted in partial fulfillment for the award of the Degree of Master of Technology in Department of School of Instrumentation (Instrumentation Engineering)Supervisor: Submitted By:Dr. Ganga Agnihotri Jitendra Singh BhadoriyaProfessor & Dean Acad. Er. No.: 11/2011MANIT, Bhopal Department of School of Instrumentation DEVI AHILYA VISHWAVIDYALAYA, INDOREJITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  2. 2. CANDIDATE’S DECLARATIONI hereby declare that the work, which is being presented in the Dissertation, entitled“Optimal Placement and Sizing Of Multi-Distributed Generation (DG) IncludingDifferent Load Models Using PSO” in partial fulfillment for the award of Degree of“Master of Technology” in Department of School of Instrumentation withSpecialization Instrumentation, and submitted to the Department of School ofInstrumentation Engineering, Devi Ahilya Vishwavidyalaya, Indore, is a record ofmy own investigations carried under the Guidance of Dr. Ganga Agnihotri,Department of Electrical Engineering, MANIT, Bhopal.I have not submitted the matter presented in this Dissertation anywhere for the awardof any other Degree. (Jitendra Singh Bhadoriya) Instrumentation Engineering Enrolment No.: 11/2011 D.A.V.V, INDOREJITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  3. 3. CERTIFICATEThis is to certify that the work which is being presented in this dissertation entitled“Optimal Placement and Sizing Of Multi-Distributed Generation (DG) IncludingDifferent Load Models Using PSO” submitted by Mr. Jitendra Singh Bhadoriya,to the Devi Ahilya Vishwavidyalaya, Indore, towards partial fulfillment of therequirements for the award of the Degree of Master of Technology inInstrumentation Engineering (Power System) is a bonafide record of the workcarried out by him under my supervision and that this work has not been submittedelsewhere for a degree. Supervisor: Dr. Ganga Agnihotri Professor & Dean Acad. MANIT, BhopalJITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  4. 4. ACKNOWLEDGEMENTFor the accomplishment of this thesis work, expression and words run short to conveymy gratitude to many individuals. This thesis work is an outcome of moral supportand persuasive interest dedicated from many individuals directly or indirectlyinvolved.Though the idea of the thesis started from characterizing a curricular obligation, neverthe less it has taken the interest of learning to ever-new heights for us. I am indebtedto MANIT, Bhopal, for providing such a forum where we can utilize and in a wayexperiment with the knowledge acquired over the complete Master of Technologycurriculum.I would like to take this opportunity in expressing immense gratitude to my guide Dr.Ganga Agnihotri, Professor, Department of Electrical Engineering, MANIT, Bhopal,for her constant inspiration, useful criticism and immense support throughout thework. I am indebted for the hard work she has put in to produce this report in the bestpossible form.I would like to extend my honour to Dr. Appu Kuttan K.K. Director, MANIT,Bhopal, Dr. R.K. Nema Head of Electrical Department, MANIT, Bhopal, for theirblessings and encouragement.I am thankful to the Staff Members of Department of Electrical Engineering, MANIT,Bhopal, for their co-operation in my work. Last but not least I would like to expressmy sincere thanks to all of my friends for their valuable support.Finally, my special thanks to my parents for their moral support and encouragement. (Jitendra Singh Bhadoriya)JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  5. 5. ABSTRACTThis work presents a multi objective performance index-based size and locationdetermination of distributed generation in distribution systems with different loadmodels. Normally, a constant power (real and reactive) load model is assumed in mostof the studies made in the literature. It is shown that load models can significantlyaffect the optimal location and sizing of distributed generation (DG) resources indistribution systems. The simulation technique based on particle swarm technology isstudied. The studies have been carried out on 38-bus distribution systems.This work proposes a multi-objective index-based approach for optimally determiningthe size and location of multi-distributed generation (multi-DG) units in distributionsystems with different load models. It is shown that the load models can significantlyaffect the optimal location and sizing of DG resources in distribution systems Theproposed function also considers a wide range of technical issues such as active andreactive power losses of the system, the voltage profile, the line loading, and theMega Volt Ampere (MVA) intake by the grid. An optimization technique based onparticle swarm optimization (PSO) is introduced. An analysis of the continuationpower flow to determine the effect of DG units on the most sensitive buses to voltagecollapse is carried out. The proposed algorithm is tested using a 38-bus radial system.After enactment of Electricity Act ‘2003 in India, a comprehensive change ishappening in Indian power sector, and power distribution utilities are going through areformation process to cope up with the regulatory change for reduction inAggregated Technical and Commercial Loss, improvement in Power Quality,Reliability of Power Supply, and Improvement in Customer Satisfaction. Smart Gridis sophisticated, digitally enhanced power systems where the use of moderncommunications and control technologies allows much greater robustness, efficiencyand flexibility than today’s power systems. In a smart grid, all the various nodes needto interconnect to share data as and where needed. Government of India has recentlyformed “Smart Grid Forum” and “Smart Grid Task Force” for enablement of smartgrid technology into Indian Power Distribution Utilities as a part of their Smart Gridinitiative to meet their growing energy demand in similar with the developed countrylike USA, Europe etc.JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  6. 6. CONTENTSCANDIDATE’S DECLARATION …………………………………………………iCERTIFICATE….......................................................................................................iiACKNOWLEDGEMENTS.......................................................................................iiiABSTRACT...................................................................................................................ivCONTENTS.................................................................................................................vLIST OF FIGURES....................................................................................................viLIST OF TABLES......................................................................................................viCHAPTER-1………………………………………………………………………...1INTRODUCTION…………………………………………………………………..11.1 Introduction……………………………………………………………………....11.2 DG types and range……………………………………………………………….21.3 Distributed Power Applications………………………………….………………..51.4 Classic ElectricityParadigm………………………………………………………61.5 The Benefits of Distributed Power.………………………………….....................7CHAPTER-2…………………………………………………………………….......10PARTICLE SWARM OPTIMIZATION………………………………...………..102.1 Introduction………………………………………...........................................….102.2 The PSO algorithm………………………………………………………….........10CHAPTER-3………………………………………………………………………...12PSAT/MATLAB RESEARCH TOOL..…………………………………………...123.1 Overview……………………………………………………………………….....12CHAPTER-4…………………………………………………………………….…..16MODELING OF SMART RADIAL SYSTEM...…………………………………164.1 Description of a Power System…………………………………………………..164.2 Important of Load Modeling………………………………………………..….16JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  7. 7. 4.3 Load models and impact indices ………………………………………………...174.4 Smart Grid Pilots in India…………………………………………………….......20CHAPTER-5………………………………………………………………………...23CONCLUSION……………………………………………………………………...23REFERENCES……………………………………………………………………...24 LIST OF FIGUREFigure 1. Distributed generation types and technologies….……………………….....3Figure 2. ElectricityParadigm………………………………………………………….7Figure 3. Main graphical user interface of PSAT…………………………………….15Figure 4. PSAT Simulink library……………………………………………..……….15Figure 5. 38-bus test system…………………………………………………………..18Figure 6. Structure of Smart Grid…………………………………………………….22 LIST OF TABLETable -1 Comparison between common energy types for power and time duration…4Table -2 Functions available on MATLAB and GNU/OCTAVE platforms……..….14Table -3 Load types and exponent values…………………………………………....17 Table -4 System and load data for 38-bus system………..………………Dec-2012JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  8. 8. Chapter-1INTRODUCTION1.1 IntroductionDistributed generation (DG) is not a new concept but it is an emerging approach forproviding electric power in the heart of the power system. It mainly depends upon theinstallation and operation of a portfolio of small size, compact, and clean electricpower generating units at or near an electrical load (customer). Till now, not all DGtechnologies and types are economic, clean or reliable. Some literature studiesdelineating the future growth of DGs are: a) The Public Services Electric and Gas Company (PSE&G), New Jersey, started to participate in fuel cells (FCs) and photovoltaics (PVs) from 1970 and micro-turbines (MTs) from 1995 till now. PSE&G becomes the distributor of Honeywell’s 75kW MTs in USA and Canada. Fuel cells are now available in units range 3–250kW size. b) The Electric Power Research Institutes (EPRI) study shows that by 2010, DGs will take nearly 25% of the new future electric generation, while a National Gas Foundation study indicated that it would be around 30%.Surveying DG concepts may include DG definitions, technologies, applications, sizes,locations, DG practical and operational limitations, and their impact on systemoperation and the existing power grid. This work focuses on surveying different DGtypes, technologies, definitions, their operational constraints, placement and sizingwith new methodology particle swarm optimization. Furthermore, we aim to present acritical survey by proposing new DG in to conventional grid to make it smart grid.JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  9. 9. 1.2 DG TYPES AND RANGEThere are different types of DGs from the constructional and technological points ofview as shown in Fig. 1. These types of DGs must be compared to each other to helpin taking the decision with regard to which kind is more suitable to be chosen indifferent situations. However, in our paper we are concerned with the technologiesand types of the new emerging DGs: micro-turbines and fuel cells. The different kindsof distributed generation are discussed below.Micro-turbine (MT)Micro-turbine technologies are expected to have a bright future. They are smallcapacity combustion turbines, which can operate using natural gas, propane, and fueloil. In a simple form, they consist of a compressor, combustor, recuperator, smallturbine, and generator. Sometimes, they have only one moving shaft, and use air or oilfor lubrication. MTs are small scale of 0.4–1m3 in volume and 20–500kW in size.Unlike the traditional combustion turbines, MTs run at less temperature and pressureand faster speed (100,000 rpm), which sometimes require no gearbox. Some existingcommercial examples have low costs, good reliability, fast speed with air foil bearingsratings range of 30–75kW are installed in North-eastern US and Eastern Canada andArgentina by Honeywell Company and 30–50kW for Capstone and Allison/GEcompanies, respectively . Another example is ABB MT: of size 100kW, which runs atmaximum power with a speed of 70,000 rpm and has one shaft with no gearbox wherethe turbine, compressor, and a special designed high speed generator are on the sameshaft.JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  10. 10. Fig. 1. Distributed generation types and technologies.Electrochemical devices: fuel cell (FC)The fuel cell is a device used to generate electric power and provide thermal energyfrom chemical energy through electrochemical processes. It can be considered as abattery supplying electric energy as long as its fuels are continued to supply. Unlikebatteries, FC does not need to be charged for the consumed materials during theelectrochemical process since these materials are continuously supplied. FC is a well-known technology from the early 1960s when they were used in the Modulated StatesSpace Program and many automobile industry companies. Later in 1997, the USDepartment of Energy tested gasoline fuel for FC to study its availability forgenerating electric power. FC capacities vary from kW to MW for portable andstationary units, respectively.Storage devicesIt consists of batteries, flywheels, and other devices, which are charged during lowload demand and used when required. It is usually combined with other kinds of DGtypes to supply the required peak load demand. These batteries are called “deepcycle”. Unlike car batteries, “shallow cycle” which will be damaged if they haveseveral times of deep discharging, deep cycle batteries can be charged and dischargeda large number of times without any failure or damage. These batteries have aJITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  11. 11. charging controller for protection from overcharge and over discharge as itdisconnects the charging process when the batteries have full charge. The sizes ofthese batteries determine the battery discharge period. However, flywheels systemscan charge and provide 700kW in 5 s.Renewable devicesGreen power is a new clean energy from renewable resources like; sun, wind, andwater. Its electricity price is still higher than that of power generated fromconventional oil sources.DG capacities: DG capacities are not restrictedly defined as they depend on the usertype (utility or customer) and/or the used applications. These levels of capacities varywidely from one unit to a large number of units connected in a modular form. Table 1 Comparison between common energy types for power and time duration Power supplied period DG type RemarksLong period supply Gas turbine and FC Provide P and Q except FC provides P stations only. Used as base load provider.Unsteady supply Renewable energy Depend on weather conditions. systems; PV arrays, Provide P only and need a source of Q WT in the network. Used in remote places. Need control on their operation in some applications.Short period supply FC storage units, Used for supply continuity. batteries, PV cells Store energy to use it in need times for a short period.JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  12. 12. 1.3 Distributed Power ApplicationsDistributed power technologies are typically installed for one or more of the followingpurposes:(i) Overall load reduction – Use of energy efficiency and other energy savingmeasures for reducing total consumption of electricity, sometimes with supplementalpower generation.(ii) Independence from the grid – Power is generated locally to meet all local energyneeds by ensuring reliable and quality power under two different models. a. Grid Connected – Grid power is used only as a back up during failure of maintenance of the onsite generator. b. Off grid – This is in the nature of stand-alone power generation. In order to attain self-sufficiency it usually includes energy saving approaches and an energy storage device for back-up power. This includes most village power applications in developing countries.(iii) Supplemental Power- Under this model, power generated by the grid isaugmented with distributed generation for the following reasons: - a. Standby Power- Under this arrangement power availability is assured during grid outages. b. Peak shaving – Under this model the power that is locally generated is used for reducing the demand for grid electricity during the peak periods to avoid the peak demand charges imposed on big electricity users.(iv) Net energy sales – Individual homeowners and entrepreneurs can generate moreelectricity than they need and sell their surplus to the grid. Co-generation could fallinto this category.(v) Combined heat and power - Under this model waste heat from a power generatoris captured and used in manufacturing process for space heating, water heating etc. inorder to enhance the efficiency of fuel utilization.(vi) Grid support – Power companies resort to distributed generation for a widevariety of reasons. The emphasis is on meeting higher peak loads without having toinvest in infrastructure (line and sub-station upgrades).JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  13. 13. 1.4 Classic Electricity Paradigm (Central Power Station Model)The current model for electricity generation and distribution in the United States isdominated by centralized power plants. The power at these plants is typicallycombustion (coal, oil, and natural) or nuclear generated. Centralized power models,like this, require distribution from the center to outlying consumers. Currentsubstations can be anywhere from 10s to 100s of miles away from the actual users ofthe power generated. This requires transmission across the distance.This system of centralized power plants has many disadvantages. In addition to thetransmission distance issues, these systems contribute to greenhouse gas emission, theproduction of nuclear waste, inefficiencies and power loss over the lengthytransmission lines, environmental distribution where the power lines are constructed,and security related issues.Many of these issues can be mediated through distributed energies. By locating, thesource near or at the end-user location the transmission line issues are renderedobsolete. Distributed generation (DG) is often produced by small modular energyconversion units like solar panels. As has been demonstrated by solar panel use in theUnited States, these units can be stand-alone or integrated into the existing energygrid. Frequently, consumers who have installed solar panels will contribute more tothe grid than they take out resulting in a win-win situation for both the power grid andthe end-user.JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  14. 14. Fig. 2: a) Classic Electricity Paradigm, b) Distributed Generation (DG) Electricity Paradigm1.5 The Benefits of Distributed PowerA) Energy consumers, power providers and all other state holders are benefited intheir own ways by the adoption of distributed power. The most important benefit ofdistributed power stems from its flexibility, it can provide power where it is neededand when it is needed.The major benefits of distributed power to the various stakeholders are as follows:JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYA
  15. 15. 1.5.1 Major Potential Benefits of Distributed Generation1.5.2 Consumer-Side Benefits: Better power reliability and quality, lower energycost, wider choice in energy supply options, better energy and load management andfaster response to new power demands are among the major potential benefits that canaccrue to the consumers.1.5.3 Grid –Side Benefits: The grid benefits by way of reduced transmission anddistribution losses, reduction in upstream congestion on transmission lines, optimaluse of existing grid assets, higher energy conversion efficiency than in centralgeneration and improved grid reliability. Capacity additions and reductions can bemade in small increments closely matching the demands instead of constructingCentral Power Plants which are sized to meet a estimated future rather than currentdemand under distributed generation.1.5.4 Benefits To Other Stake Holders: Energy Service Companies get newopportunities for selling, financing and managing distributed generation and loadreduction technologies and approaches. Technology developers, manufacturers andvendors of distributed power equipment see opportunities for new business in anexpanded market for their products. Regulators and policy maker’s support distributedpower as it benefits consumers and promotes competition.B) The following are among the more important factors that contributed to theemergence of distributed generation as a new alternative to the energy crisis thatsurfaced in the USA.i. Energy Shortage –States likes California and New York that experienced energyshortages decided to encourage businesses and homeowners to install their owngenerating capacity and take less power from the grid. The California Public UtilitiesCommission for instance approved a programme of 125 US million $ incentivesprogramme to encourage businesses and homeowners to install their own generatingcapacity and take less power from the grid. In the long run the factors enumeratedbelow would play a significant part in the development of distributed generation.ii. Digital Economy –Though the power industry in the USA met more than 99% ofthe power requirements of the computer based industries, these industries found thateven a momentary fluctuation in power supply can cause computer crashes. TheJITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  16. 16. industries, which used computer, based manufacturing processes shifted to their ownback-up systems for power generation.iii. Continued Deregulation of Electricity Markets – The progressive deregulation ofthe electricity markets in the USA led to violent price fluctuations because the powergenerators, who were not allowed to enter into long-term wholesale contracts, had topass on whatever loss they suffered only on the spot markets. In a situation like that inCalifornia where prices can fluctuate by the hour, flexibility to switch onto and off thegrid alone gives the buyer the strength to negotiate with the power supplier on astrong footing. Distributed generation in fact is regarded as the best means of ensuringcompetition in the power sector.C) Both in the USA and UK the process of de-regulation did not make smoothprogress on account of the difficulties created by the regulated structure of the powermarket and a monopoly enjoyed the dominant utilities.D) In fact, the current situation in the United States in the power sector is compared tothe situation that arose in the Telecom Sector on account of the breakup of AT&TCorporation’s monopoly 20 years ago. In other words distributed generation is arevolution that is caused by profound regulatory change as well as profound technicalchange.JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  17. 17. Chapter-2Particle Swarm Optimization2.1 IntroductionParticle swarm optimization (PSO) is a population based stochastic optimizationtechnique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by socialbehavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such asGenetic Algorithms (GA). The system is initialized with a population of randomsolutions and searches for optima by updating generations. However, unlike GA, PSOhas no evolution operators such as crossover and mutation. In PSO, the potentialsolutions, called particles, fly through the problem space by following the currentoptimum particles. The detailed information will be given in following sections.Compared to GA, the advantages of PSO are that PSO is easy to implement and thereare few parameters to adjust. PSO has been successfully applied in many areas:function optimization, artificial neural network training, fuzzy system control, andother areas where GA can be applied.2.2 The PSO algorithmAs stated before, PSO simulates the behaviors of bird flocking. Suppose the followingscenario: a group of birds are randomly searching food in an area. There is only onepiece of food in the area being searched. All the birds do not know where the food is.But they know how far the food is in each iteration. So whats the best strategy to findthe food? The effective one is to follow the bird which is nearest to the food.PSO learned from the scenario and used it to solve the optimization problems. InPSO, each single solution is a "bird" in the search space. We call it "particle". All ofparticles have fitness values which are evaluated by the fitness function to beoptimized, and have velocities which direct the flying of the particles. The particlesfly through the problem space by following the current optimum particles.JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  18. 18. PSO is initialized with a group of random particles (solutions) and then searches foroptima by updating generations. In every iteration, each particle is updated byfollowing two "best" values. The first one is the best solution (fitness) it has achievedso far. (The fitness value is also stored.) This value is called pbest. Another "best"value that is tracked by the particle swarm optimizer is the best value, obtained so farby any particle in the population. This best value is a global best and called gbest.When a particle takes part of the population as its topological neighbors, the bestvalue is a local best and is called lbest.After finding the two best values, the particle updates its velocity and positions withfollowing equations.v[] = v[] + c1 * rand() * (pbest[] - present[]) + c2 * rand() * (gbest[] - present[])present[] = persent[] + v[]Where v[] is the particle velocity, persent[] is the current particle (solution). pbest[]and gbest[] are defined as stated before, rand () is a random number between (0,1). c1,c2 are learning factors usually c1 = c2 = 2.JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  19. 19. Chapter-3PSAT/MATLAB RESEARCH TOOL3.1 OverviewPower System Analysis is an analysis that is so important nowadays. It is not onlyimportant in economic scheduling, but also necessary for planning and operation for asystem. Based on that, in recently years, there are many researches, newdevelopments and analysis was introduced to people in order to mitigate the problemsthat involving Power System Analysis such as Load Flow Analysis, Fault Analysis,Stability Analysis and Optimal Dispatch on Power Generation.i) Load Flow Analysis is important to analyze any planning for power systemimprovement under steady state conditions such as to build new power generationcapacity, new transmission lines in the case of additional or increasing of loads, toplan and design the future expansion of power systems as well as in determining thebest operation of existing systems.ii) Fault Analysis is important to determine the magnitude of voltages and linecurrents during the occurrence of various types of fault.iii) Stability Analysis is necessary for reliable operation of power systems to keepsynchronism after minor and major disturbances.iv) Optimal Dispatch is to find real and reactive power to power plants to meet loaddemand as well as minimize the operation cost.All the analysis discussed above is an importance tool involving numerical analysisthat applied to a power system. In this analysis, there is no known analytical methodto solve the problem because it depends on iterative technique. Iterative technique isone of the analysis that using a lot of mathematical calculations which takes a lot oftimes to perform by hand. So, to solve the problems, the development of this toolboxbased on MATLAB 7.8 with Graphical User Interface (GUI) will help the analysisbecome quick and easy.JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  20. 20. The PSAT kernel is the power flow algorithm, which also takes care of the statevariable initialization. Once the power flow has been solved, the user can performfurther static and/or dynamic analyses. These are: 1) Continuation Power Flow (CPF); 2) Optimal Power Flow (OPF); 3) Small signal stability analysis; 4) Time domain simulations.PSAT deeply exploits Matlab vectorized computations and sparse matrix functions inorder to optimize performances. Furthermore PSAT is provided with the mostcomplete set of algorithms for static and dynamic analyses among currently availableMatlab-based power system softwares (see Table II). PSAT also contains interfaces toUWPFLOW and GAMS which highly extend PSAT ability to solve CPF and OPFproblems, respectively.In order to perform accurate and complete power system analyses, PSAT supports avariety of static and dynamic models, as follows:- Power Flow Data: Bus bars, transmission lines and transformers, slack buses, PVgenerators, constant power loads, and shunt admittances.- Market Data: Power supply bids and limits, generator power reserves, and powerdemand bids and limits.- Switches: Transmission line faults and breakers.- Measurements: Bus frequency measurements.- Loads: Voltage dependent loads, frequency dependent loads, ZIP (polynomial)loads, thermostatically controlled loads, and exponential recovery loads [14].- Machines: Synchronous machines (dynamic order from 2 to 8) and induction motors(dynamic order from 1 to 5).- Controls: Turbine Governors, AVRs, PSSs, Over-excitation limiters, and secondaryvoltage regulation.- Regulating Transformers: Under load tap changers and phase shifting transformers.JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  21. 21. - FACTS: SVCs, TCSCs, SSSCs, UPFCs.- Wind Turbines: Wind models, constant speed wind turbine with squirrel cageinduction motor, variable speed wind turbine with doubly fed induction generator, andvariable speed wind turbine with direct drive synchronous generator.- Other Models: Synchronous machine dynamic shaft, subsynchronous resonancemodel, solid oxide fuel cell, and subtransmission area equivalents.Besides mathematical algorithms and models, PSAT includes a variety of additionaltools, as follows: 1) User-friendly graphical user interfaces; 2) Simulink library for one-line network diagrams; 3) Data file conversion to and from other formats; 4) User defined model editor and installer; 5) Command line usage. TABLE 2 Functions available on MATLAB and GNU/OCTAVE platformsJITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  22. 22. Fig. 3. Main graphical user interface of PSAT. Fig. 4. PSAT Simulink library.JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  23. 23. Chapter-4MODELING OF SMART RADIALSYSTEM4.1 Description of a Power SystemA power system must be safe, reliable, economical, benign to the environment andsocially acceptable. The power system is subdivided into Generation, Transformer,Transmission and Sub-Transmission, Distribution and Loads. The following sectionwill examine each of the sub-system in detailed.4.1.1 DistributionThe distribution system is the part that the sub-transmission lines typically delivertheir power to locations called substations where the voltage is transformeddownward to a voltage that is required by the customers. The voltage of thedistribution system is between 4.6KV and 25KV.4.2 Important of Load ModelingThe power system engineer bases decisions concerning system reinforcements andsystem performance in large part on the results of power flow and stability simulationstudies. Representation inadequacies that cause under or over building of the systemor degradation of reliability could prove to be costly. In performing power systemanalysis, models must be developed for all pertinent system components, includinggenerating stations, transmission and distribution equipment, and load devices. Muchattention has been given to models for generation and transmission/distributionequipment. The representation of the loads has received less attention and continuesto be an area of greater uncertainty. Many studies have shown that load representationcan have significant impact on analysis results. Therefore, efforts directed atimproving load modelling are of major importance.4.3 Load models and impact indicesJITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  24. 24. The optimal allocation and sizing of DG units under different voltage-dependent load dependentmodel scenarios are to be investigated. Practical voltage-dependent load models, i.e., dependentresidential, industrial, and commercial, have been adopted for investigations. The loadmodels can be mathematically expressed as: asWhere Pi and Qi are real and reactive power at bus i, Poi and Qoi are the active andreactive operating points at bus i, Vi is the voltage at bus i, and α and β are real andreactive power exponents. In the constant power model conventionally used in powerflow studies, α = β = 0 is assumed. The values of the real and reactive exponents usedin the present work for industrial, residential, and commercial loads are given in Table3. Table 3 Load types and exponent values.JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYA
  25. 25. Fig. 5. 38-bus test system. TABLE 4 System and load data for 38-bus systemJITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  26. 26. 4.4 Smart Grid Pilots in IndiaThe following functionalities have been proposed in the 8 pilot projectsJITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYA
  27. 27. 4.4.1 Smart grid as distributionSmart Grid is the modernization of the electricity delivery system so that it monitors,protects and automatically optimizes the operation of its interconnected elements –from the central and distributed generator through the high-voltage network and highdistribution system, to industrial users and building automation systems, to energy systemstorage installations and to end use consumers and their thermostats, electric vehicles, end-useappliances and other household devices. Smart grid is the integration of informationand communications system into electric transmission and distribution networks. TheSmart Grid in large, sits at the intersection of Energy, IT and TelecommunicationTechnologies. The smart grid (Refer Fig 6) delivers electricity to consumers using )two-way digital technology to enable the more efficient management of consum way consumers’JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYA
  28. 28. end uses of electricity as well as the more efficient use of the grid to identify andcorrect supply demand-imbalances instantaneously and detect faults in a “self-healing” process that improves service quality, enhances reliability, and reduces costs.The emerging vision of the smart grid encompasses a broad set of applications,including software, hardware, and technologies that enable utilities to integrate,interface with, and intelligently control innovations.Some of the enabling technologies & business practice that make smart griddeployments possible include: • Smart Meters • Meter Data Management • Field area networks • Integrated communications systems • IT and back office computing • Data Security • Electricity Storage devices • Demand Response • Distributed generation • Renewable energy Fig 6: Structure of Smart GriJITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  29. 29. Chapter-5CONCLUSIONS 1) Here the problem of DG placement & capacity has presented. 2) PSO methodology used for multi dg placement. 3) IT will make power grid in to smart grid. 4) DG have advantage of islanding, it make consumer less dependent on grid. 5) DG can be work either individually or grid connected so it forms decentralized system.REFERENCES [1] Book of Swarm Intelligence by JamesKennedy, YuhuSh. [2] THE ELECTRICITY ACT, 2003. [3] http://www.sciencedirect.com/ [4] Smart Grid Vision & Roadmap for India (benchmarking with other countries) – Final Recommendations from ISGF. [5] Islanding Protection of Distribution Systems with Distributed Generators – A Comprehensive Survey Report S.P.Chowdhury, Member IEEE. [6] Distributed Power Generation: Rural India – A Case Study, Anshu Bharadwaj and Rahul Tongia, Member, IEEE. [7] Interconnection Guide for Distributed Generation. [8] Empirical study of particle swarm optimization. [9] POWER SYSTEM ANALYSIS EDUCATIONAL TOOLBOX USING MATLAB 7.1 .JITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA
  30. 30. [10] Power System Load Modeling The School of Information Technology and Electrical Engineering The University of Queensland by Wen Zing Adeline Chan. [11] Smart grid initiative for power distribution utility in India Power and Energy Society General Meeting, 2011 IEEE 24-29 July 2011 Energy & Utilities Group of Capgemini India Private Ltd., Kolkata, India [12] Distributed generation technologies, definitions and benefits Electric Power Systems Research 71 (2004) 119–128 [13] Multiobjective Optimization for DG Planning With Load Models IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 24, NO. 1, FEBRUARY 2009 [14] Ministry of Power, 2003a. Annual Report 2002–2003, Government of India, New Delhi. [15] Ministry of Power, 2003b. Discussion Paper on Rural Electrification Policies, November 2003, Government of India, New Delhi. [16] http://www.powermin.nic.in/ [17] http://www.dg.history.vt.edu/ch1/introduction.html [18] http://ieeexplore.ieee.org [19] http://www.swarmintelligence.org [20] http://umpir.ump.edu.my/360/ [21] http://www.mnre.gov.in [22] http://www.isgtf.in [23] http://www.mathworks.inJITENDRA SINGH BHADORIYA , SCHOOL OF INSTRUMENTATION,DEVI AHILYAUNIVERSITY,INDORE INDIA

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